code: def __init__(self, y, y_hat, protected, privileged, verbose=False):
      self.regression_dict = {}
      self.subgroup_metrics = {}
      self.subgroup_metric_comparison = {}
  
      subgroups = set(protected)
  
      for subgroup in subgroups:
          sub_y = y[np.where(protected == subgroup)]
          sub_y_hat = y_hat[np.where(protected == subgroup)]
          self.regression_dict[subgroup] = {
              "y": sub_y,
              "y_hat": sub_y_hat,
          }
  
          self.subgroup_metrics[subgroup] = {
              "mean_error": (sum(sub_y_hat - sub_y)) / (len(y)),
              "mae": utils.mae(sub_y_hat, sub_y),
              "rmse": utils.rmse(sub_y_hat, sub_y),
              "mean_prediction": np.mean(sub_y_hat),
          }
  
          self.subgroup_metric_comparison[subgroup] = {
              "mae_ratio": self.subgroup_metrics[subgroup].get("mae")
              / self.subgroup_metrics[privileged].get("mae"),
              "rmse_ratio": self.subgroup_metrics[subgroup].get("rmse")
              / self.subgroup_metrics[privileged].get("rmse"),
              "mean_prediction_ratio": self.subgroup_metrics[
                  subgroup
              ].get("mean_prediction")
              / self.subgroup_metrics[privileged].get(
                  "mean_prediction"
              ),
          }
  
imports: from copy import deepcopy
  import numpy
  import pandas
  from .None import checks
  from ...None import _global_checks
  from ..._explainer import helper
  from ...model_explanations._model_performance import utils
label_desc: Use image node_id 1 to create a new RegressionDict object  with example: obj = RegressionDict(y, y_hat, protected, privileged, verbose)